State-of-the-Art Wayside Condition Monitoring Systems for Railway Wheels: A Comprehensive Review
Передовые бортовые системы мониторинга состояния колес железнодорожного подвижного состава: всесторонний обзор
2023-01-01
SCID: 54.1/vv8763qt
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machine learning / deep learning for onboard monitoringrailway wheel condition monitoringstrain gaugesvision sensorswayside condition monitoring systems
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Abstract (AI)
In recent decades, there has been a constant demand for faster, longer, and safer railway networks. This also brings challenges for condition monitoring systems in modern railway vehicles. More specifically, critical parts of railway vehicles like wheels degrade over time due to various operational and environmental reasons. Different dynamic effects such as skidding/sliding over the track and the presence of contamination between wheel-rail cause various wheel defects. Faulty wheels ultimately lead to the derailment of railway vehicles. To avoid worst situations like railway derailments, various research has been conducted for developing efficient condition monitoring systems for railway wheels. In addition, there have been some commercial condition monitoring products that can be deployed with railway vehicles. These systems incorporate various sensors such as strain gauges and vision sensors to collect data for diagnosis and prognosis. Various methods have been explored but yet there is a broad research gap in terms of developing advanced onboard condition monitoring systems. With the progress in technology, advanced systems with Machine Learning/Deep Learning methods can provide more efficient and robust condition monitoring of dynamic railway systems. Considering the need for advancement in condition monitoring systems for railway vehicles, a comprehensive review of existing condition monitoring systems for railway wheels is conducted in this paper. The review is aimed at understanding the feasibility and potential of new methods for modern railways. This paper provides a detailed overview of studies on the existing wayside systems and reports their advantages and disadvantages concerning its recently emerging counterpart on-board monitoring systems. Data acquisition systems and analysis methods are critically reviewed which could assist in developing more efficient and reliable condition monitoring systems for railway wheels. This article also reviews the current progress of wayside systems and their limitations. The article is targeted at the researchers and engineers working in this domain, who can pave the way for developing advanced and cost-effective condition monitoring systems for railway wheels using modern technologies.
Key Findings
1
Existing wayside condition monitoring systems use sensors like strain gauges and vision sensors for data collection and diagnosis/prognosis.
2
Machine Learning and Deep Learning hold potential to provide more efficient and robust onboard condition monitoring for dynamic railway systems.
3
Railway wheel degradation from skidding/sliding and contamination causes defects that can lead to derailments, motivating condition monitoring systems.
4
The review critically assesses data acquisition and analysis methods, summarizing advantages and limitations of wayside versus emerging onboard systems to inform future development.
5
There is a broad research gap in developing advanced onboard condition monitoring systems despite available commercial wayside products.
Research Object
Wayside condition monitoring systems for railway wheels
Research Subject
Performance, capabilities, data acquisition and analysis methods, advantages/limitations, and feasibility of advanced (including ML/DL and on-board vs wayside) condition monitoring approaches for detecting and predicting railway wheel defects
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2023-01-01
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